1. Field of the Invention
The present invention relates to a system and method for validating data for a machine.
2. Background Art
Machine monitoring is a process by which a controller controlling a machine—e.g., a programmable logic controller (PLC) controlling a machine tool in a production facility—runs a program to automatically report the performance status of that machine. This reporting may be in the form of a transfer of information in standardized data blocks to a machine management system, such as a factory information system (FIS). Machine monitoring systems (MMS's) can provide important information about the condition of a machine on a plant floor. Examples of such information include: the number of units produced in a given time period, process cycle times, error conditions, machine failure conditions, blockages, etc. Based on this information, plant personnel can make educated decisions about resource allocations, production levels, and resolution of machine stoppages. The system is, however, only valuable if the information being provided is accurate. When the information is inaccurate, the system cannot be used to its full potential, causing inefficient work practices and decision making.
In order to determine the validity of the data captured in an MMS, an individual, such as a subject matter expert, may review thousands of pieces of information to determine anomalies in the data that may indicate that some of the data is inaccurate. Such a subject matter expert may learn to identify these anomalies only after years of training and experience. Moreover, regardless of the level of training and experience of a particular individual, the sheer quantity of data generated in a large scale production facility may be impossible to completely review. This can lead to data inaccuracies being missed, inaccurate data being erroneously assumed to be accurate, and a loss of confidence in the data by decision-making personnel.
As an example of the amount of data that may need to be reviewed, consider that if one machine on one assembly line generates 4000-5000 blocks of data per day, and each block of data consists of 32 status registers denoting the status of the machine at any given time, approximately 150,000 data entries will be generated, and will need to be reviewed. It is worth noting that the approximately 150,000 data entries represent only one machine on one assembly line. In a particular facility, there may be dozens of assembly lines, each consisting of 5-10 machines. Thus, for a single production facility for a single day, there may be tens of millions of data entries generated that need to be reviewed for accuracy by a subject matter expert.
As a practical matter, the subject matter expert cannot handle such an overwhelming amount of data, and is therefore required to limit the focus to a single area. Moreover, logistical considerations present limitations on this type of analysis—i.e., there are far more production facilities and machines than there are subject matter experts to cover them. Thus, the current state of the art requires a labor and time intensive effort executed by a few highly trained individuals who, despite their skills, are limited in the amount of data they can validate.
Therefore, a need exists for a system and method for validating data for a machine that takes advantage of electronic processing speed and the knowledge and skill of subject matter experts to quickly and accurately analyze large quantities of data to determine their validity. This, in turn, will allow an FIS or other machine management system to perform its tasks with some assurance that the data on which it is basing its decisions are accurate.
One of the applications where a subject matter expert may be used to review machine monitoring data is prior to a particular machine being implemented in a production setting. For example, a subject matter expert may visit the machine builder prior to a new machine being certified for delivery to a production facility. In this environment, the machine may be operated by itself, or in conjunction with other machines as part of a work cell or assembly line, and machine monitoring data gathered during this preproduction operation. The subject matter expert can then review and analyze the recorded data to determine its accuracy. Once the machine has passed any required certification test, and the subject matter expert has determined that it is providing accurate machine monitoring data, it may then be shipped to a customer for use in a production facility.
As discussed above, the machine monitoring data may be sent to an FIS to use the data to generate reports on which management decisions can be based. Unfortunately, setting up the interface between the MMS and the FIS often requires interruption of the machine data validation and machine certification at the machine builder. In a practical sense, this requires the FIS to be temporarily installed onsite at the machine builder's location to fine tune the interface between the MMS and the FIS. This process is costly, time consuming, and disruptive to the testing required of the newly built machine.
Therefore, it would be desirable to have a system and method for validating data for a machine that also facilitates recordation of machine data that can later be output to an FIS at an offsite facility. For example, it would be desirable to prepare the interface between the MMS and the FIS at the production facility where the FIS will be permanently installed. This would avoid the undesirable extra steps of moving the FIS to the machine builder's location and disrupting the other testing on the newly built machine.